Dynamic Retrieval Augmented Generation: Real-time Information Update for Enhanced Conversational Continuity

Fredric Cliver
3 min readOct 13, 2023

1. Introduction

In the realm of conversational AI, the ability to generate coherent, relevant, and dynamic responses is paramount. Traditionally, models have either relied on a fixed knowledge base or external retrieval mechanisms that are static in nature. The Dynamic Retrieval Augmented Generation (Dynamic RAG) approach offers a groundbreaking method, enabling models to search and update information in real-time as the conversation progresses. This not only enhances conversational continuity but also allows for a deeper and richer interaction experience.

2. Conceptual Framework of Dynamic RAG

2.1 Traditional RAG:

Retrieval Augmented Generation combines the benefits of retrieval-based and generation-based models. In a typical RAG, the model retrieves relevant documents or passages and then generates a response based on the retrieved information.

2.2 Dynamic RAG:

Building upon the foundations of traditional RAG, Dynamic RAG introduces real-time information retrieval. Rather than relying solely on a static set of retrieved data, the model can update its knowledge on-the-fly, either before a user’s prompt or during its response generation.

3. Usage and Implementation



Fredric Cliver

Majored in Physics, self-taught and worked in the IT industry as a Dev/Design/Planning for 11 years. And I had run my Startup for 3 years. I fancy a ☔️ 🇬🇧